{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import the libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the dataset\n",
    "dataset = pd.read_csv('C:/Users/Susan/Desktop/Salary_Data.csv')\n",
    "X = dataset.iloc[:, :-1].values\n",
    "y = dataset.iloc[:, 1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Susan\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "# split data into training set and test set\n",
    "from sklearn.cross_validation import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 1/3, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting simple linear regression to training set \n",
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting the test set results\n",
    "y_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZsAAAEWCAYAAACwtjr+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XucHFWd///XOzOQhGsSCLcECBqUm6LLyAKrLD+5egX3\noW5ckbiLRokurroouOsi6iJeULxNJAtKFAQ0omH9chVUVAg4AZZwlQBCEgIJkBDukOTz+6NOM909\nPTM9k66u6en38/Hox1Sdup2qmalPf06dqlJEYGZmlqcxRVfAzMxGPwcbMzPLnYONmZnlzsHGzMxy\n52BjZma5c7AxM7PcOdhYQ0j6q6TDiq5HK5L0tKRXFF2PcpK+IunfGrzOuvdzJB6TcpLGSrpb0uSi\n69IqHGzsZZLeKOl6SU9KekLSnyS9oeh65UHSeZJeTCe10uf/iqhLRGwREfcXse1a0gn0OODsNH6I\npGUbu96h7OcIPCa/k/Sh0nhEvAD8EDi5uFq1FgcbA0DSVsCvge8Ck4ApwGnACzlvtzPP9Q/ia+mk\nVvrs28yNF7zvA/kgcFlEPFfvAiN4X/L0U2CmpLFFV6QVONhYyasAIuLCiFgfEc9FxFURcRuApFdK\nulbS45Iek3SBpAm1ViRpf0k3SFojaYWk70natGx6SPqYpHuBeyV9X9KZVeu4VNIna6x7jqRvVJUt\nkPSpNPxZScslPSXpHkmHDvVASPpHSQ+kAIykt0h6pNRkkup/oqT707H4uqQxZcv/i6S7JK2WdKWk\nXfvb97Ky6Wl4rKRvSHpI0qOSfiBpfJp2iKRlkj4taWU6tv9ctu7xks6U9GDKTv9YtuwBKWtdI+n/\nJB0ywCF4C/D7tNzmwOXATmUZ4E6SviBpvqTzJa0FPljn7720n+el3/v/S7+rGyW9cpjzHpF+109K\n6pb0+/IspOp3u7+kHklr0/H9Ztm0msdI0n8DbwK+l/b/ewARsQxYDRwwwLG0kojwxx+ArYDHgXlk\nJ5uJVdOnA4cDY4HJwHXAWWXT/woclob3I/sH7ASmAXcB/1Y2bwBXk2VQ44H9gYeBMWn6tsCzwPY1\n6nkwsBRQGp8IPAfsBLw6TdspTZsGvLKf/T0P+PIAx+OCNM82qW5vr6r/b1P9dwH+AnwoTTsaWALs\nmfb/P4Hr+9v3srLpafhbwKVp+pbA/wJfSdMOAdYBXwQ2Ad6ajtPENP37wO/IstIO4KD0+5qSfrdv\nJfuCeXgan9zPvq8C3lA2fgiwrGqeLwAvAcekdY6v8/c+vez4P55+953peF801HnT38pa4B/StE+k\nen2on327AfhAGt4COCAND3iM0nHts870uzqx6P/fVvgUXgF/Rs4nnSDPA5alk9ql1Djhp3mPAW4p\nG/8rKdjUmPffgF+WjQfw5qp57gIOT8MfJ2vGqbUuAQ8BB6fxDwPXpuHpwErgMGCTQfb1POB5YE3Z\nZ17Z9AlpO4uBs6uWDeCosvHZwDVp+HLg+LJpY8gCwq4D7Hukugt4hrIACRwIPJCGDyELrJ1l01eS\nneDHpGn71tjXzwI/qSq7EpjZz7F5CdijbPwQageb6wY5xrV+7+UB5JyyaW8F7h7qvGTXlm6o+vtY\nSv/B5jqy5uFth3KM6D/YXAD8V6P/F0fjx81o9rKIuCsiPhgRU4F9yLKFswAkbS/potREtRY4n+xb\nZR+SXiXp16npaS1weo15l1aNzwOOTcPHAj/pp44BXAS8LxX9E9k/PBGxhOwE9wVgZarvTgPs8jci\nYkLZZ2bZdtYAP0/H4cway5bX/0GyYwWwK/Dt1BSzBniC7AQ4pZ9ly00GNgMWlS1/RSoveTwi1pWN\nP0v2DX1bYBxwX4317gq8p7TOtN43Ajv2U4/VZFnVYCr2o87fe7lHauzHUOfdqbwe6e9joM4Mx5M1\nGd8t6c+S3p7Kh3qMSrYk+6Jig3CwsZoi4m6yb5T7pKLTyb5tviYitiILCOpn8TnA3cDuad7P1Zi3\n+nHj5wNHS9qXLMP61QDVuxB4d7oW8rfAL8rq/dOIeCPZySOArw6wnn5Jeh3wL2lb36kxy85lw7uQ\nNbVBduL7SFUQGx8R15fN39+j1h8jy072Llt264gY6CRcvuzzwCtrTFtK9q29vE6bR8QZ/azrNtI1\nvEHqW11ez++90VYAU0sjklQ+Xi0i7o2I9wHbkf1tzE/XpQY7Rv0dgz2BQnoxthoHGwNA0h7pwvPU\nNL4zWfawMM2yJfA08KSkKcBJA6xuS7J29Kcl7QGcMNj2I7vY+meyjOYXMUBPqIi4hezkeg5wZcpC\nkPRqSW9W1jvoebIT94bBtl1N0jiy4Pc54J+BKZJmV812kqSJ6Th9Arg4lf8AOEXS3mldW0t6Tz3b\njYgNwP8A35K0XVp+iqQj61z2h8A30wX8DkkHpmNxPvAOSUem8nGps0F/J+XLgL8vG38U2EbS1oNU\nY8i/9wb4f8BrJB2jrEfcx4Ad+ptZ0rGSJqfjVcpINjD4MXoUeEXVuqaQXVtbiA3KwcZKniLLEm6U\n9AzZP9DtwKfT9NOAvwGeJPsHv2SAdf07WfPWU2Qnz4sHmLfcPOA19NOEVuWnZNdmflpWNhY4gywQ\nPUL27fWUAdbxGVXeZ/NYKv8KsDQi5kR2P8WxwJcl7V627AJgEXAr2fE4FyAifkn2jfmi1JR0O1mH\ni3p9lqyDwcK0/G/IOj7U49/JrjH9maz57qtknS6WknVc+BzZxf+lZF8W+vv//zHwVqWebCnLvRC4\nPzUx9dc0Odzf+7BFxGPAe4CvkV3Q3wvoof8u+0cBd0h6Gvg2MCOynpeDHaNvk2XTqyWVMt1/IrvO\nl+vtAaNFqUePWeEkHUz2DXPXGMF/mJKCrKloSdF1yYuk04GVEXFW0XUZCmVd0JcB74+I3+a4nbFk\nzWcHR8TKvLYzmrTjjVg2AknahKw56pyRHGjaRUR8rug61Cs1M95I1mx6Etl1olybtlI2s0ee2xht\n3IxmhZO0J1n7+Y6k3m9mQ3AgWS+8x4B3AMcMdM3PiuFmNDMzy50zGzMzy52v2STbbrttTJs2rehq\nmJm1lEWLFj0WEYO+asHBJpk2bRo9PT1FV8PMrKVIerCe+dyMZmZmuXOwMTOz3DnYmJlZ7hxszMws\ndw42ZmaWOwcbMzPLnYONmZnlzsHGzKzNHH44HHVUc7fpmzrNzNrEDTfAQQcVs20HGzOzNvDa18Li\nxb3jD9Z133/juBnNzGwUu+46kHoDzYwZEAG77NLcejizMTMbpfbcE+6+u3d86VKYOrWYujizMTMb\nZa65JstmSoHmAx/IspmpU4HZs6GzM5uhszMbbwJnNmZmo8j06XDffb3jK1bADjukkdmzYc6c3onr\n1/eOd3fnWi9nNmZmo8Dll2fJSinQfOhDWTbzcqABmDu39sL9lTeQMxszsxa3667w0EO946tWwbbb\n1phx/fraK+ivvIFyy2wk/VDSSkm3l5V9XdLdkm6T9EtJE8qmnSJpiaR7JB1ZVr6fpMVp2nckKZWP\nlXRxKr9R0rSyZWZKujd9Zua1j2ZmRVqwIMtmSoFm9uwsm6kZaAA6OoZW3kB5NqOdB1Tfo3o1sE9E\nvBb4C3AKgKS9gBnA3mmZbkmlvZ8DfBjYPX1K6zweWB0R04FvAV9N65oEnAr8LbA/cKqkiTnsn5lZ\nITZsgClT4Jhjessefxy+//1BFpw1a2jlDZRbsImI64Anqsquioh1aXQhUOqEdzRwUUS8EBEPAEuA\n/SXtCGwVEQsjIoAfA8eULTMvDc8HDk1Zz5HA1RHxRESsJgtwTX4wg5lZPubPzxKRhx/Oxj/5ySyb\nmTSpjoW7u+GEE3ozmY6ObDznzgFQ7DWbfwEuTsNTyIJPybJU9lIari4vLbMUICLWSXoS2Ka8vMYy\nFSTNAmYB7NLsO5zMzIZgwwbYcUdYuTIbl2DNGthqqyGuqLu7KcGlWiG90ST9B7AOuKCI7ZdExNyI\n6IqIrsmTJxdZFTOzfl14YZaElALNySdnwWfIgaZATc9sJH0QeDtwaGoaA1gO7Fw229RUtpzeprby\n8vJllknqBLYGHk/lh1Qt87tG7oOZWTNs2ACTJ8MT6YLEmDHw5JOwxRbF1ms4mprZSDoK+Azwzoh4\ntmzSpcCM1MNsN7KOADdFxApgraQD0vWY44AFZcuUepq9G7g2Ba8rgSMkTUwdA45IZWZmLeNHP8qy\nmVKg+fznsx7KrRhoIMfMRtKFZBnGtpKWkfUQOwUYC1ydejAvjIiPRsQdkn4G3EnWvPaxiCh1/J5N\n1rNtPHB5+gCcC/xE0hKyjggzACLiCUlfAv6c5vtiRFR0VDAzG6k2bICJE2Ht2my8szPLZjbbrNh6\nbSz1tmS1t66urujp6Sm6GmbWxubOhY98pHf8S1+C//zP4upTD0mLIqJrsPn8BAEzs4KtWwcTJsAz\nz2Tjm26aZTPjxhVbr0bys9HMzAr0ve/BJpv0BpqvfQ1eeGF0BRpwZmNmVoh167Kuy889l42PG5dl\nM5tuWmy98uLMxsysyb75zSybKQWas87KhkdroAFnNmZmTfPii7DlltlPgM03z54C0NkGZ2JnNmY2\nehT0Fsp6nHEGjB3bG2i6u+Hpp9sj0IAzGzMbLQp8C+VAnn8+y2bWpUcQb7lldqNmuwSZEmc2ZjY6\nFPgWyv6cdhqMH98baM45J7tZs90CDTizMbPRosC3UFZ79tmsp1lp0xMnwmOPZc82a1dtvOtmNqoU\n+BbKcv/xH9mF/1KgmTcvazZr50ADzmzMbLSYNavymk15eRM8/XSWzZSeADZ5MjzyiINMiQ+DmY0O\nBb6F8qSTsgv/pUBz0UXZu2ccaHo5szGz0aPJb6Fcsya7HlOyww6wfLmDTC0+JGZmw3DiiZWBZv58\nWLHCgaY/zmzMzIbgvvtg+vTe8Z13hoceKq4+rcIx2MysTttuWxloFixwoKmXMxszs0HcdRfstVdl\nmd87OTTObMzMBrD11pWB5tRTHWiGw5mNmVkNt94Kr399ZZmDzPA5szEzq7LZZpWB5itfcaDZWM5s\nzMySP/8Z9t+/ssxBpjGc2ZiZkb1rpjzQnHWWA00jObMxs7b2xz/Cm95UWeYg03jObMysbXV2Vgaa\nH/zAgSYvDjZm1nauvjp7c3T5q24i4CMfqWPhEfzq6ZHMwcbM2sqYMXDEEb3j8+YNIZspvXq6FKVK\nr552wBmUg42ZtYVf/zpLRsoDSwQcd9wQVjICXz3dKhxszGzUk+Ad7+gdv/jiYV6bGUGvnm41DjZm\nNmrNn58FmnIR8N73DnOFI+TV063IwcbMRiUJ3vOe3vFf/aoBPc36e8V0k1493cocbMxsVPnRj2pn\nM0cf3YCVF/jq6VbnmzrNbNSoDjJXXAFHHtngjTT51dOjhTMbM2t5n/507Wym4YHGhs2ZjZm1tOog\nc/758P73F1MX658zGzNrSR/9aO1sxoFmZHJmY2YtpzrIXHIJvOtdxdTF6pNbZiPph5JWSrq9rGyS\npKsl3Zt+TiybdoqkJZLukXRkWfl+khanad+Rsj8zSWMlXZzKb5Q0rWyZmWkb90qamdc+mllzHXts\n7WzGgWbky7MZ7TzgqKqyk4FrImJ34Jo0jqS9gBnA3mmZbkmlu6TmAB8Gdk+f0jqPB1ZHxHTgW8BX\n07omAacCfwvsD5xaHtTMrDVJcMEFveNXXOEnNLeS3IJNRFwHPFFVfDQwLw3PA44pK78oIl6IiAeA\nJcD+knYEtoqIhRERwI+rlimtaz5waMp6jgSujognImI1cDV9g56ZtYh/+Af3NBsNmn3NZvuIWJGG\nHwG2T8NTgIVl8y1LZS+l4ery0jJLASJinaQngW3Ky2ssU0HSLGAWwC677DK8PTKz3FQHmd//Hg4+\nuJi62MYprDdaylQKTYIjYm5EdEVE1+TJk4usilnryuH9LkceWTubcaBpXc0ONo+mpjHSz5WpfDmw\nc9l8U1PZ8jRcXV6xjKROYGvg8QHWZWaNlsP7XSS46qre8Ztu8rWZ0aDZweZSoNQ7bCawoKx8Ruph\nthtZR4CbUpPbWkkHpOsxx1UtU1rXu4FrU7Z0JXCEpImpY8ARqczMGq2B73d505tqZzNveMMw6mUj\nTm7XbCRdCBwCbCtpGVkPsTOAn0k6HngQeC9ARNwh6WfAncA64GMRUXpBxGyynm3jgcvTB+Bc4CeS\nlpB1RJiR1vWEpC8Bf07zfTEiqjsqmFkjNOD9Lhs29H1C/+LFsM8+G1EvG3EUzk8B6Orqip6enqKr\nYdZaOjtrB5aODli3btDFu7pg0aLKMp+SWoukRRHRNdh8flyNmQ3fMN/vsmFD1mRWHmj+8hcHmtHM\nj6sxs+ErPWp/7twsw+noyALNAI/g33tvuPPO3nEpCz42ujnYmNnGqfP9LuvWwSabVJb99a+w6675\nVMtGFjejmVnuXvGKykDT0ZE1mTnQtA9nNmaWmxdfhLFjK8tWrIAddiimPlYcZzZmrSiHu/Ybbaed\nKgPN2LFZNuNA056c2Zi1mtJd+yWlu/ahrmsneXv2Wdh888qyxx+HSZOKqY+NDM5szFpNA+/ab7Rt\ntqkMNFtskWUzDjTmzMas1TTgrv1GW7sWtt66suzJJ2GrrYqpj408DjZmraajo/+79gtQ/TyzSZOy\nZjOzcm5GM2s1w7xrf1gG6IjwyCN9A80zzzjQWG3ObMxazTDu2h+WAToiaE7ltsaMKbQVz1qAH8SZ\n+EGcZlVqPGTzQXZhGg9WlD33HIwb18yK2UhS74M4ndmYWW1VgUZVL9bddFN44YVmVshama/ZmFlt\nqcPBXby6T6B56SUHGhsaBxszq23WLESwF3e/XLQ5TxMnzKbTbSI2RA42ZtbHzTf37QSwXp08fcJn\nRsRTCqz1+PuJmVXo/76Zwd+8adYfZzZmBsCf/tQ30Kxf7/tmrDGc2ZhZnyCz006wfHkxdbHRyZmN\nWRu78sq+gSbCgcYaz5mNWZuqDjKvfCUsWVJMXWz0c2Zj1mZ++cva2YwDjeXJmY1ZG6kOMq95Ddx2\nWzF1sfZSV2YjqZhnl5tZQ5x/fu1sxoHGmqXezOZeSb8AfhQRd+ZZITNrrOogc+CBcP31xdTF2le9\n12z2Bf4CnCNpoaRZkvwOPrMR7Oyza2czDjRWhLqCTUQ8FRH/ExEHAZ8FTgVWSJonaXquNTSzIZPg\nox/tHT/ssCzQmBWl7ms2kt4p6ZfAWcCZwCuA/wUuy7F+ZjYEZ55ZO5u5+upi6mNWUvc1G+C3wNcj\nojwJny/p4MZXy8yGqjrIvOtdcMklxdTFrNqgmU3qiXZeRBxfFWgAiIgTc6mZmdXlC1+onc040NhI\nMmiwiYj1wNubUBczGyIJTjutd/wDH/C1GRuZ6m1G+5Ok7wEXA8+UCiPi5lxqZWYDet/74KKLKssc\nZGwkqzfYvC79/GJZWQBvbmx1zKxfs2fD3LlofeV7ZT76UZgzp6A6mdWprmATEf9f3hUxswHMns3b\n5ryVy6h8S2acMNtvzrSWoKgz95b0NmBvYFypLCK+2P8SraWrqyt6enqKroZZTdUdAN7PTzif46Cj\nA9b5DZpWHEmLIqJrsPnqvc/mB8A/Av8KCHgPsOtGVO6Tku6QdLukCyWNkzRJ0tWS7k0/J5bNf4qk\nJZLukXRkWfl+khanad+Rsn9JSWMlXZzKb5Q0bbh1NSvSwQfX6GmGskAD2as0zVpAvY+rOSgijgNW\nR8RpwIHAq4azQUlTgBOBrojYB+gAZgAnA9dExO7ANWkcSXul6XsDRwHdZQ8GnQN8GNg9fY5K5cen\nuk4HvgV8dTh1NSuSBH/4Q+/4bL5HUBV5OvyMXGsN9Qab59LPZyXtBLwE7LgR2+0ExkvqBDYDHgaO\nBual6fOAY9Lw0cBFEfFCRDwALAH2l7QjsFVELIysLfDHVcuU1jUfOLSU9ZiNdK9/fY1s5oTZfJ9/\n7TvzrFnNqZTZRqo32Pxa0gTg68DNwF+BC4ezwYhYDnwDeAhYATwZEVcB20fEijTbI8D2aXgKsLRs\nFctS2ZQ0XF1esUxErAOeBLaprkt6oGiPpJ5Vq1YNZ3fMGkqCW2/tHT/55NSlubsbTjihN5Pp6MjG\n3TnAWkS9vdG+lAZ/IenXwLiIeHI4G0zXYo4GdgPWAD+XdGzV9kJS7ncNRMRcYC5kHQTy3p5Zf171\nKrj33sqyPn13ursdXKxlDRhsJP3DANOIiOE8EOMw4IGIWJXWcwlwEPCopB0jYkVqIluZ5l8O7Fy2\n/NRUtjwNV5eXL7MsNdVtDTw+jLqa5a66yez00+GUU4qpi1leBsts3jHAtACGE2weAg6QtBnZtaBD\ngR6yJxPMBM5IPxek+S8Ffirpm8BOZB0BboqI9ZLWSjoAuBE4Dvhu2TIzgRuAdwPXRr19vM2aZMoU\nePjhyjL/ldpoNWCwiYh/bvQGI+JGSfPJrv2sA24ha8raAviZpOOBB4H3pvnvkPQz4M40/8fS89oA\nZgPnAeOBy9MH4FzgJ5KWAE+Q9WYzGzGqs5nvfhc+/vFi6mLWDL6pM/FNndYMkybB6tWVZc5mrJWN\n6Js6zdqRVBlo5s1zoLH2Ue+DOA+KiNdKui0iTpN0Jr1NVmY2gM03h2efrSxzkLF2M9ybOtexcTd1\nmrUFqTLQXHKJA421p3ozm9JNnV8DFqWyc/Kpklnr22STvs/HdJCxdjZgZiPpDZJ2iIgvRcQash5j\ni4Gfkz1zzMzKbNiQZTPlgeaqqxxozAZrRjsbeBFA0sFk98CcTfb4l7n5Vs2stYwZ0/e5mBFw+OHF\n1MdsJBks2HRExBNp+B+BuRHxi4j4PDA936qZtYZ167Jspjx7uf56ZzNm5Qa7ZtMhqTM9zPJQoPwR\ns/Ve7zEbtWo9S9xBxqyvwTKbC4HfS1pA1iPtDwCSppM1pZm1peef7xtoFi50oDHrz2CPq/lvSdeQ\ndXO+quz5YmOg1ss1zEY/ZzNmQzdoU1hELKxR9pd8qmM2cq1dC1tvXVm2eDHss08x9TFrJb7uYlYH\nZzNmG6feJwiYtaXHHusbaO6/34HGbKic2Zj1w9mMWeM4szGr8tBDfQPNihUONGYbw5mNWRlnM2b5\ncGZjBtxzT99As3q1A41ZozizsbbnbMYsf85srG3dfHPfQPPMMw40ZnlwsLG2JMF++1WWRcBmmw2w\n0OzZ0NmZLdzZmY2bWV0cbKyt/PGPfbOZF16oI5uZPRvmzIH167Px9euzcQccs7oo3GYAQFdXV/T0\n9BRdDcvRRl2b6ezsDTTlOjr6vpLTrI1IWhQRXYPN58zGRr3LLusbaNavH+K1mVqBZqByM6vgYGOj\nmgRve1tlWUT2Vs0hqX4FZ3W5r+eYDcjBxkalCy9sQDZTbtas/st9PcdsUL7PxkadXO6b6e7Ofs6d\nmwWTjo4s0HR3Z5lMLXPn9i5n1uac2VjjFdSkdPbZfQNNRAPvm+nuzjoDRGQ/S4HE13PMBuXMxhqr\n1KRUUmpSgly/5VcHGQk2bMhtc5U6OvrvqWZmgDMba7S5c4dWvpG+9rXa2UzTAg0MfD3HzABnNtZo\nTWxSqg4ym26a3aDZdANdzzEzwJmNNdpgXYQb4Mtfrp3NFBJoSvq7nmNmgIONNVrOTUoSfP7zvePb\nbecHZ5q1Agcba6zubjjhhN5MpqMjG9/Ib/onnVQ7m3n00Y1arZk1ia/ZWON1dze0Gak6yOy2G9x/\nf8NWb2ZN4MzGRqxZs2pnMw0LNH7EjFnTOLOxEak6yOyzDyxe3MANFHQ/kFm7KiSzkTRB0nxJd0u6\nS9KBkiZJulrSvennxLL5T5G0RNI9ko4sK99P0uI07TtSdoqSNFbSxan8RknTmr+XbSCHzGDGjNrZ\nTEMDDTT9fiCzdldUM9q3gSsiYg9gX+Au4GTgmojYHbgmjSNpL2AGsDdwFNAtqdSPdg7wYWD39Dkq\nlR8PrI6I6cC3gK82Y6faSg4Pn5Tg4ot7xw88MMeeZn7EjFlTNT3YSNoaOBg4FyAiXoyINcDRwLw0\n2zzgmDR8NHBRRLwQEQ8AS4D9Je0IbBURCyN7A9yPq5YprWs+cGgp67EGaWBmcNRRtbOZ668fRr3q\n1YT7gcysVxGZzW7AKuBHkm6RdI6kzYHtI2JFmucRYPs0PAVYWrb8slQ2JQ1Xl1csExHrgCeBbaor\nImmWpB5JPatWrWrIzrWNBmUGElx5Ze/4EUc06b4ZP2LGrKmKCDadwN8AcyLi9cAzpCazkpSp5H7K\niYi5EdEVEV2TJ0/Oe3Ojy0ZmBgcdVDubKQ88ucrpfiAzq62IYLMMWBYRN6bx+WTB59HUNEb6uTJN\nXw7sXLb81FS2PA1Xl1csI6kT2Bp4vOF70s42IjOQ4IYbesff/e6CngLgR8yYNU3Tg01EPAIslfTq\nVHQocCdwKTAzlc0EFqThS4EZqYfZbmQdAW5KTW5rJR2QrsccV7VMaV3vBq5N2ZI1yjAyg9e+tnY2\n8/Of51hPMxsRiuqN9q/ABZJuA14HnA6cARwu6V7gsDRORNwB/IwsIF0BfCwiShcGZgPnkHUauA+4\nPJWfC2wjaQnwKaqa6axBhpAZSJXdlz/0oQGymXq7VPumTLPWERH+RLDffvuF1eGEEyI6OrIXYHZ0\nZOMD2G230rsyez+Drr96Aei7nXrnM7NcAT1RxzlW4dYlALq6uqKnp6foaoxs1Xfdl/TTfFbdZPap\nT8GZZw6yjc7O/t96uW7d0Oczs1xJWhQRXYPO52CTcbCpQ50n+GOOgQULKmep+89soNuhyldS73xm\nlqt6g40fxGn1q+PeGqky0HzhC0M899fbpdo3ZZq1FAcbq98AJ/jDD6/d0+zUU4e4jXq7VPumTLOW\n4mBj9evnRK716/jNb3rH//ct3UTHMHuJ1dul2jdlmrUUX7NJfM2mTrNnZ88/W7+eA7mehRxYMTlO\nGFonAjNrbe4gMEQONkNT3WR2zTXw5jfjXmJmbabeYOOXp9mQdHXBokWVZRXfV/zofjOrwddsrC4b\nNmTZTHmguf76Gj3N3EvMzGpwsLFBHXhg31gRkZX34V5iZlaDm9GsXxs29A0yd94Je+45wEKlTgCp\nEwEdHVkz+rgIAAAMEElEQVSgcecAs7bmYGM17bsv3HZbZVndfUm6ux1czKyCg41VWLcONtmksuz+\n+2G33Yqpj5mNDr5mYy971asqA82YMVk240BjZhvLmY3x4oswdmxl2dKlMHVq7fnNzIbKwabN7bJL\nFlhKNtkkCz5mZo3kYNOmnn8exo+vLHv0Udhuu2LqY2ajm6/ZtKHtt68MNJttll2bcaAxs7w4s2kj\nTz8NW25ZWbZ6NUyYUEx9zKx9OLNpExMmVAaaCROybMaBxsyawZnNKLdmDUycWFn21FOwxRbF1MfM\n2pMzm1Fss80qA81222XZjAONmTWbg00zzJ6dvedlOG+uHIY1a7JNPfdcb9lzz2W9zczMiuBgk7fZ\n6c2Vpfe5rF+fjecUcPbdtzKb2XnnLJsZNy6XzZmZ1cVv6kxye1Nnk95c+dhjMHlyZdlLL2WbNzPL\nS71v6nRmk7cmvLlyjz0qA8373pdlMw40ZjZS+HSUt46O/jObjfTII7DjjpVl69dnD9A0MxtJfFrK\nW05vrtxtt8pAc/zxWTbjQGNmI5Ezm7w1+M2VDz0Eu+5aWbYeMea8DtjUb8Q0s5HJ34Obobs76wwQ\nkf0cZkCYMqUy0Hyc7xIo+yXm3MvNzGxjONi0gAceyO6befjh3rL16uS7nNh35rlzm1cxM7M6OdiM\ncJMnwyte0Tv+7/+ers1E/r3czMwaxddsRqi77oK99qosq7glKsdebmZmjebMZgSaMKEy0Hz+81WB\nBnLr5WZmlgdnNiPIbbdlj5sp1+8DHhrcy83MLE/ObEaILbaoDDSnnz5AoClpUC83M7O8FRZsJHVI\nukXSr9P4JElXS7o3/ZxYNu8pkpZIukfSkWXl+0lanKZ9R5JS+VhJF6fyGyVNa/b+1aunJ+tp9swz\nvWURcMopxdXJzKzRisxsPgHcVTZ+MnBNROwOXJPGkbQXMAPYGzgK6JZUugo+B/gwsHv6HJXKjwdW\nR8R04FvAV/PdleEZNw7e8Ibe8TPPrCObqVeTX2tgZjaQQoKNpKnA24BzyoqPBual4XnAMWXlF0XE\nCxHxALAE2F/SjsBWEbEwskdX/7hqmdK65gOHlrKeXNV5gv/Tn7JZXnihtywCPvWpBtajia81MDMb\nTFGZzVnAZ4ANZWXbR8SKNPwIsH0angIsLZtvWSqbkoaryyuWiYh1wJPANtWVkDRLUo+knlWrVm3U\nDtV7gu/shDe+sXe8u7uB2UxJfzd2+oZPMytI04ONpLcDKyNiUX/zpEwl9xftRMTciOiKiK7J1S+D\nGapBTvDXXptlM+W3xkTACSds3GZrasJrDczMhqKIzObvgHdK+itwEfBmSecDj6amMdLPlWn+5cDO\nZctPTWXL03B1ecUykjqBrYHH89iZlw1wgn/b2+DQQ3uLzjsvh2ymXH83dvqGTzMrSNODTUScEhFT\nI2Ia2YX/ayPiWOBSYGaabSawIA1fCsxIPcx2I+sIcFNqclsr6YB0Pea4qmVK63p32ka+mVKNE/k9\n7I4ILrustywCZs7sM2tj+YZPMxthRtJ9NmcAh0u6FzgsjRMRdwA/A+4ErgA+FvHyg8Fmk3UyWALc\nB1yeys8FtpG0BPgUqWdbrqpO5IdzFXvwl5fHb7kl52ymXHd31j5XCoAdHdm478Mxs4Io7y/8raKr\nqyt6eno2biWzZ3PHD65jn7j95aK//3v43e82brVmZiOVpEUR0TXYfCMps2l5T3+tuyLQ3H67A42Z\nGfjZaA216aaw554wdSpcdVXRtTEzGzkcbBpo003hzjuLroWZ2cjjZjQzM8udg42ZmeXOwcbMzHLn\nYGNmZrlzsDEzs9w52JiZWe4cbMzMLHcONmZmljs/Gy2RtAp4sOh6DNO2wGNFV6JA7bz/7bzv0N77\nP1L2fdeIGPSFYA42o4CknnoehDdatfP+t/O+Q3vvf6vtu5vRzMwsdw42ZmaWOweb0WFu0RUoWDvv\nfzvvO7T3/rfUvvuajZmZ5c6ZjZmZ5c7BxszMcudg08Ik7Szpt5LulHSHpE8UXadmk9Qh6RZJvy66\nLs0maYKk+ZLulnSXpAOLrlOzSPpk+pu/XdKFksYVXac8SfqhpJWSbi8rmyTpakn3pp8Ti6zjYBxs\nWts64NMRsRdwAPAxSXsVXKdm+wRwV9GVKMi3gSsiYg9gX9rkOEiaApwIdEXEPkAHMKPYWuXuPOCo\nqrKTgWsiYnfgmjQ+YjnYtLCIWBERN6fhp8hONlOKrVXzSJoKvA04p+i6NJukrYGDgXMBIuLFiFhT\nbK2aqhMYL6kT2Ax4uOD65CoirgOeqCo+GpiXhucBxzS1UkPkYDNKSJoGvB64sdiaNNVZwGeADUVX\npAC7AauAH6VmxHMkbV50pZohIpYD3wAeAlYAT0bEVcXWqhDbR8SKNPwIsH2RlRmMg80oIGkL4BfA\nv0XE2qLr0wyS3g6sjIhFRdelIJ3A3wBzIuL1wDOM8GaURknXJo4mC7g7AZtLOrbYWhUrsntYRvR9\nLA42LU7SJmSB5oKIuKTo+jTR3wHvlPRX4CLgzZLOL7ZKTbUMWBYRpUx2PlnwaQeHAQ9ExKqIeAm4\nBDio4DoV4VFJOwKknysLrs+AHGxamCSRtdnfFRHfLLo+zRQRp0TE1IiYRnZx+NqIaJtvtxHxCLBU\n0qtT0aHAnQVWqZkeAg6QtFn6HziUNukcUeVSYGYangksKLAug3KwaW1/B3yA7Fv9renz1qIrZU3z\nr8AFkm4DXgecXnB9miJlc/OBm4HFZOexlnp0y1BJuhC4AXi1pGWSjgfOAA6XdC9ZtndGkXUcjB9X\nY2ZmuXNmY2ZmuXOwMTOz3DnYmJlZ7hxszMwsdw42ZmaWOwcbG9WU+aOkt5SVvUfSFQXX6WeSbpN0\nYtW0L0taXtaV/VZJW+Zcnyvz3oaZuz7bqCdpH+DnZM+O6wRuAY6KiPs2Yp2dEbFumMtOBX6TntZc\nPe3LwGMRcdZw6zaEeojsHNCOz5azJnNmY6NeRNwO/C/wWeC/gB9HxH2SZkq6KWUP3ZLGAEiaK6kn\nvS/lv0rrSTfTnSHpFuBd6Z0qd6YMpc+jciSNlzRP0mJJN0s6OE26Ctg1bbeux6xIOknS3DT8urTN\n8SkTmidpYXqvyb+ULXNy2r/bSvshaXqq8wXAHcCOab8mpOl9jomkTklr0r7/n6QbJG2X5t9B0oK0\njf+T9Lf9rWdIvzQbfSLCH39G/QfYHLiH7I7zscA+wK+AzjR9LvBPaXhS+tkJ/AHYK40vAz5Vts4V\nwKZpeEKNbX4WmJuG9wYeBDYFpgO39lPPLwPLgVvT5zepfAzwJ+CdZJnZAWXz3wyMA7ZLddweeCvQ\nDSgtewXZ88Omkz0lu6tsm8uACf0dk3QcAnhLKv8mcHIa/gXw8bLjtdVAx9af9v101h2VzFpYRDwj\n6WLg6Yh4QdJhwBuAnqw1ifHA0jT7+9LjQDrJniq8F73PHbu4bLV3AOdLWkB2cq32RuDraft3SHqY\n7GT/4iDV/XpUNaNFxAZJHyQLQN+LiIVlk38VEc8Dz0u6Lu3XYcBbyAITwBbAq8ge1nhfRPTU2O5A\nx+S5iLg8DS8C3pSGDyG9uCyyZsW1gxxba1MONtZONtD77hsBP4yIz5fPIGl3srd/7h8Ra1LzWPkr\nh58pGz4S+HuybONzkl4bEetzq30WLJ4mC4Dlqi+8Btn+fTkizi2fIGk6lftQMZnax6STygC5nspz\nR/X2a67H2pvbUa1d/QZ4r6RtASRtI2kXsmagp8i+oe9IFlD6kNQBTI2Ia8le4LYt2Rsjy/0BeH+a\nf09gR2DJcCqr7B0u3yRrCpsiqfytjMdIGitpMlnG0QNcCRyv9EI1SVNL+zqA/o7JQH4LfDTN3yFp\nq2Gux0Y5ZzbWliJisaTTgN+ki9cvkZ00e8iazO4mu8byp35W0Qn8NHUZHgN8I7JXc5f7LnC2pMVp\n/cdFxIupaWkgJ6Ums5J3AP8NfDuyjg3/nOr9xzT9duD3wDbAqRHxKHCZpD2AhWl7T5Fdf+nXAMdk\noFcufxz4H0kfAdYBH4mIm/pZz0OD7biNXu76bNbCmtlV2mxjuBnNzMxy58zGzMxy58zGzMxy52Bj\nZma5c7AxM7PcOdiYmVnuHGzMzCx3/z9B3wQZ18AWtAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b7c008c4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the training set\n",
    "plt.scatter(X_train, y_train, color = 'red')\n",
    "plt.plot(X_train, regressor.predict(X_train), color = 'blue')\n",
    "plt.title('Salary vs Experience (training set)')\n",
    "plt.xlabel('Years of Experience')\n",
    "plt.ylabel('Salary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZsAAAEWCAYAAACwtjr+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/j/YUWjEH8OHGAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b7c04ca048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the test set \n",
    "plt.scatter(X_test, y_test, color = 'red')\n",
    "plt.plot(X_train, regressor.predict(X_train), color = 'blue')\n",
    "plt.title('Salary vs Experience (test set)')\n",
    "plt.xlabel('Years of Experience')\n",
    "plt.ylabel('Salary')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
